CN111242424A - Quality data processing method and device - Google Patents

Quality data processing method and device Download PDF

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CN111242424A
CN111242424A CN201911419937.1A CN201911419937A CN111242424A CN 111242424 A CN111242424 A CN 111242424A CN 201911419937 A CN201911419937 A CN 201911419937A CN 111242424 A CN111242424 A CN 111242424A
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于钊
滕景竹
赵雪骞
钱梦迪
门业堃
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State Grid Corp of China SGCC
State Grid Beijing Electric Power Co Ltd
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Abstract

The invention discloses a quality data processing method and device. Wherein, the method comprises the following steps: obtaining initial ranking results of a plurality of power grid providers based on initial quality scores of the plurality of power grid providers, wherein the initial quality scores are determined based on at least one quality index of the power grid providers; determining the credibility of each power grid supplier based on the effective sample number of each power grid supplier; and correcting the credibility of each power grid supplier based on the initial quality scores of the power grid suppliers, and acquiring the actual ranking results of the power grid suppliers. The invention solves the technical problems that the acceptance degree of the whole power grid enterprise to the suppliers cannot be reflected due to the lack of the space dimension parameter of the supplier coverage network province number, so that some suppliers which supply a large amount of centralized supplies to a few network provinces companies have higher ranking and can not reflect the real quality condition of the suppliers.

Description

Quality data processing method and device
Technical Field
The invention relates to the field of electric power, in particular to a method and a device for processing quality data.
Background
The power grid company increases the investment of power grid construction year by year, the quantity of network access equipment materials is huge every year, if the quality of the equipment is not strictly closed, once unqualified equipment materials provided by a bad supplier enter the power grid to operate, the potential hazards are brought to the safe and stable operation of the power grid, and even safety accidents are caused to cause huge losses to the society. However, the production quality is not strictly controlled by the equipment manufacturers at present; the power grid enterprise lacks a relatively perfect supplier performance evaluation system, and the equipment operation quality information cannot be effectively fed back to an equipment bidding and network accessing link, so that the quality problem of the power grid equipment is prominent. Therefore, a set of performance evaluation algorithm of the power grid equipment supplier in the operation and inspection link needs to be established urgently, the equipment operation quality information is comprehensively fed back to the material purchasing link, and an important optimal basis is provided for power grid bidding and purchasing.
The system and the details of the power grid company are more in terms of quality of materials and management of suppliers, but the evaluation method of the quality of the equipment of the suppliers is single mainly aiming at the explanation of the quality of the materials and the related responsibility, flow and requirement of the management of the suppliers in the power grid company. At present, the main method is based on a power grid equipment quality evaluation algorithm of a Wilson confidence interval, and the method is characterized in that the suppliers are scored and ranked by integrating basic information such as the number of the suppliers, the number of the suppliers and the like and quality information such as quality defects, non-stop, faults and the like which occur in 5 years, and then the grade division range of the suppliers is determined according to a normal distribution principle.
Although the power grid equipment quality evaluation algorithm based on the Wilson confidence interval solves the reliability problem of small sample capacity, the Martian effect of the power grid equipment quality evaluation algorithm can enable some high-quality small manufacturers to be ranked all the time and cannot compete with large manufacturers by means of high-quality equipment. In addition, the current algorithm only covers the device basic information of two dimensions of quantity and time, but lacks the space dimension parameter of the number of the network provinces covered by the suppliers, so that the acceptance of the whole power grid enterprise to the suppliers cannot be reflected, and certain suppliers which supply a large amount of products to a few network provinces have higher ranking and cannot reflect the real quality condition of the suppliers.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a quality data processing method and device, which are used for at least solving the technical problems that the acceptance of the whole power grid enterprise to a provider cannot be reflected due to the lack of a space dimension parameter of the provider coverage grid province number, so that some providers which supply a large amount of products to a few grid province companies in a centralized manner have higher ranks and cannot reflect the real quality conditions of the providers.
According to an aspect of an embodiment of the present invention, there is provided a method for processing quality data, including: obtaining initial ranking results of a plurality of power grid providers based on initial quality scores of the plurality of power grid providers, wherein the initial quality scores are determined based on at least one quality index of the power grid providers; determining the credibility of each power grid supplier based on the effective sample number of each power grid supplier; and correcting the credibility of each power grid supplier based on the initial quality scores of the power grid suppliers, and acquiring the actual ranking results of the power grid suppliers.
Optionally, the quality index includes at least one of the following: the power grid supplier generates a defective deduction point, a non-stop deduction point, a fault deduction point and a reporting deduction point.
Optionally, a confidence interval determined by the effective sample number of each grid provider is used as the confidence, wherein the larger the capacity of the effective sample number is, the narrower the corresponding confidence interval is, and the smaller the difference between the lower limit value and the average sample value is; the smaller the capacity of the effective sample number is, the wider the confidence interval is, and the larger the difference between the lower limit value and the sample mean value is.
Optionally, before, the method further comprises: obtaining the sample number n of any power grid supplieriThe provincial coverage of the Hewei is ci(ii) a Based on the number of samples niThe provincial coverage of the Hewei is ciDetermining a valid number of samples n for the grid supplierei。
Optionally, the correcting the credibility of each power grid provider based on the initial quality scores of the plurality of power grid providers, and the obtaining the actual ranking results of the plurality of power grid providers includes: performing differential compression on the confidence interval radius of the power grid supplier by using a compression ratio model; and correcting the credibility of the power grid supplier based on the result of the differential compression.
Optionally, the compression ratio model is characterized by the following formula:
Figure BDA0002352062010000031
the method comprises the steps of obtaining a confidence interval lower limit of Pdawn, wherein p is an initial score of a supplier, Pdawn is a confidence interval lower limit of p, namely a final score of the supplier, n represents sample capacity, the number of devices, the number of years or the number of hundreds of years are selected according to the sample capacity of the supplier, z represents a z statistical constant corresponding to a confidence level of 1- α, and when the confidence level is 95%, the z statistical constant is 1.96.
Optionally, after correcting the credibility of each power grid provider based on the initial quality scores of the plurality of power grid providers and obtaining the actual ranking results of the plurality of power grid providers, the method further includes: and taking the power grid supplier coverage power grid province number c as a sample number influence factor, and correcting the current reliability of the power grid supplier after repair by using the compression ratio model again.
Optionally, after obtaining the actual ranking results of the plurality of grid providers, the method further includes: and classifying the plurality of power grid suppliers according to the actual ranking result by using a ranking algorithm based on the confidence interval, and adjusting the number of classification categories in the classification result according to the equipment evaluation fineness and the number of suppliers.
According to another aspect of the embodiments of the present invention, there is also provided a quality data processing apparatus, including: an obtaining module, configured to obtain initial ranking results of a plurality of power grid providers based on initial quality scores of the plurality of power grid providers, wherein the initial quality scores are determined based on at least one quality indicator of the power grid providers; the determining module is used for determining the credibility of each power grid supplier based on the effective sample number of each power grid supplier; and the correcting module is used for correcting the credibility of each power grid supplier based on the initial quality scores of the power grid suppliers and acquiring the actual ranking results of the power grid suppliers.
According to another aspect of the embodiments of the present invention, there is also provided a non-volatile storage medium, which is characterized in that the non-volatile storage medium includes a stored program, and the program controls a device in which the non-volatile storage medium is located to execute the processing method of the quality data when running.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including a processor and a memory; the memory is stored with computer readable instructions, and the processor is used for executing the computer readable instructions, wherein the computer readable instructions execute the processing method of the quality data.
In the embodiment of the invention, the initial ranking results of a plurality of power grid providers are obtained based on the initial quality scores of the power grid providers, the credibility of each power grid provider is determined based on the effective sample number of each power grid provider, the credibility of each power grid provider is corrected based on the initial quality scores of the power grid providers, and the actual ranking results of the power grid providers are obtained, so that the purpose of correcting the credibility of the power grid quality is achieved, and the technical problems that the space dimension of the number of provinces covered by the provider is lacked, the recognition degree of the whole power grid enterprise to the provider cannot be reflected, and some providers which supply a large number of provinces in a small number of power grid companies in a centralized manner have higher values and cannot reflect the real ranking conditions of the suppliers are solved.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a flow chart of a method of processing quality data according to an embodiment of the invention;
fig. 2 is a block diagram of a quality data processing apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In accordance with an embodiment of the present invention, there is provided a method embodiment of a method of processing quality data, it being noted that the steps illustrated in the flowchart of the figure may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than that presented herein.
Fig. 1 is a method for processing quality data according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
step S102, obtaining initial ranking results of a plurality of power grid suppliers based on initial quality scores of the plurality of power grid suppliers, wherein the initial quality scores are determined based on at least one quality index of the power grid suppliers.
Specifically, initial quality scores of a plurality of power grid suppliers are obtained, the quality scores are determined by at least one quality index, all the quality scores are sorted by the processor, and ranking results are stored and output for subsequent processing and analysis processes.
Optionally, the quality index includes at least one of the following: the power grid supplier generates a defective deduction point, a non-stop deduction point, a fault deduction point and a reporting deduction point.
Comprehensively analyzing the factors such as the number of equipment, the equipment operation time, the quality defect, the fault, the non-stop and the quality event report, and the like, and formulating the following supplier scoring mechanism:
(1) counting of years
Let the initial and final years in the period in which the supplier evaluation was carried out be Yl and Yu, respectively. Then, in the supplier a with the number of shipped devices n, the number ti of device stations with a certain shipped year yi is:
Figure BDA0002352062010000051
total years TA for supplier a is:
Figure BDA0002352062010000052
(2) supplier initial score calculation
Starting from the equipment operation, the equipment operation score is increased in an arithmetic progression by an initial value s0 and a step length f, and the score sj in the j-th year is as follows:
sj＝s0+f×(j-1) (3)
if the device has defects in the jth' year, the year is divided into:
sj'＝sd(4)
wherein sd is the initial score of defect occurrence in the current year (different from the initial score of defect-free years), d is the correction deduction value (obtained according to the corresponding equipment evaluation guide), and k is the deduction coefficient (see formula 14 for details of values).
The next year of the defect of the equipment is regarded as the equipment is put into operation again, the score of the year is newly calculated from s0, and the operation score Si of the equipment after y years of the equipment operation is as follows:
Figure BDA0002352062010000061
in summary, the supplier device operation score S Σ is:
Figure BDA0002352062010000062
after the operation scores of the suppliers are obtained, considering the conditions of defects, faults, non-stop, quality event notification and the like of the suppliers, carrying out quantification processing on the operation scores:
the credit for the defect occurrence of the supplier is:
D＝∑d (7)
the calculated credit value for the non-stop in ns occurrences of the quotients is:
Ds＝ns×ds(8)
the calculated deduction value of the supplier for nf failures is:
Df＝nf×df(9)
the calculated deduction value of the supplier for nbp province company announcements is:
Dbp＝nbp×dbp(10)
the calculated credit value of the supplier for nbs times of notification by national network company is:
Dbs＝nbs×dbs(11)
the calculated deduction value of the supplier for the familial defect of the nd province company is as follows:
Dd＝nd×dd(12)
in the formulas (7) to (12), ds is a single deduction value of non-stop of equipment, df is a single deduction value of fault, dbp is a single deduction value notified by a company generating province, dbs is a single deduction value notified by a company generating country, and dd is a single deduction value of a family defect of the company generating province.
For a certain supplier with equipment years T, its initial score p is:
Figure BDA0002352062010000063
aiming at different deduction conditions of various equipment, a dynamic adjustment factor is introduced, so that the problem that the running condition or the defect fault condition of the equipment affects the evaluation result in a unilateral way is solved, and the quality condition of the equipment is comprehensively reflected by evaluation. Balancing the weight of the equipment operation score and the defect deduction value, and setting a deduction coefficient k:
Figure BDA0002352062010000064
where r is the ratio of the total score Σ (D + Df + Ds + Dbp + Dbs + Dd) for a certain type of evaluation fineness to the defect-free operation score Sp, and rmax and rmin are the maximum and minimum values, respectively, for all evaluation fineness.
2.2.3 calculating the number of valid samples
Let n be the number of samples of the ith supplier for a certain evaluation finenessiThe net province coverage is ciThen, the average net province sample number giIs ni/ciThe maximum average number of net provinces samples of the suppliers with the net province coverage reaching the threshold is gmaxAnd thus the number of valid samples neiCan be defined as:
Figure BDA0002352062010000071
from the above formula, when the net province coverage of a certain supplier does not reach the threshold of the fineness and the average net province sample number is larger than gmaxWhen the number of valid samples of the supplier is not equal to the number of original samples niAnd is equal to gmaxWith its net province coverage degree of ciTo reduce the number of samples the supplier participates in, thereby reducing its first wilson score.
And step S104, determining the credibility of each power grid supplier based on the effective sample number of each power grid supplier.
Specifically, since the correction operation needs to be performed according to the credibility, in the embodiment of the present invention, in order to obtain the specific credibility of each grid provider, the number of valid samples of each grid provider needs to be utilized and analyzed.
Optionally, a confidence interval determined by the effective sample number of each grid provider is used as the confidence, wherein the larger the capacity of the effective sample number is, the narrower the corresponding confidence interval is, and the smaller the difference between the lower limit value and the average sample value is; the smaller the capacity of the effective sample number is, the wider the confidence interval is, and the larger the difference between the lower limit value and the sample mean value is.
Specifically, in order to obtain a confidence interval, namely a confidence interval, determined by the number of valid samples of each power grid provider, the embodiment of the invention adopts a wilson confidence interval ranking algorithm, and corrects the basic indexes of the provider initial score ranking through the confidence interval (namely, the confidence). The larger the sample capacity is, the narrower the confidence interval is, and the smaller the difference value between the lower limit value and the sample mean value is; the smaller the sample capacity is, the wider the confidence interval is, and the larger the difference value between the lower limit value and the sample mean value is, so that the accuracy problem of small sample capacity evaluation is well solved.
Optionally, before, the method further comprises: obtaining the sample number n of any power grid supplieriThe provincial coverage of the Hewei is ci(ii) a Based on the number of samples niThe provincial coverage of the Hewei is ciDetermining a valid number of samples n for the grid supplierei。
Specifically, in order to solve the Martian effect existing in the traditional Wilson ranking algorithm, a compression ratio improved algorithm model is introduced, and the confidence interval radius of a supplier is compressed in a differentiated mode, so that the ranking of the supplier with small sample number and outstanding equipment quality is improved. The compression ratio model comprehensively considers that the average deduction value lambda of each year in the evaluation fineness range is DΣ/T∑(DΣTo evaluate the total deduction, T, of all quality problems occurring within the range of finenessΣIn order to evaluate the total number of years of equipment of all suppliers in the fineness range), the average operating life x of the suppliers' equipment is T/N (T is the number of years of equipment of the suppliers, N is the number of equipment of the suppliers), the number of effective samples N of the suppliers and the deduction value D are as follows:
Figure BDA0002352062010000081
the compression ratio model can differentially improve the ranking of high-quality suppliers, and in the same evaluation fineness, the lifting amplitude of the suppliers is in negative correlation with the deduction value of the suppliers and is in positive correlation with the sample number of the suppliers; the score increase of the supplier is positively correlated with the annual average deduction value of the evaluation fineness among different evaluation fineness.
The lower limit of the Wilson confidence interval after introducing the compression ratio is expressed as:
Figure BDA0002352062010000082
the method comprises the following steps of obtaining a confidence interval lower limit of Pdawn, wherein p is an initial score of a supplier, Pdawn is a confidence interval lower limit of p, namely a final score of the supplier, n represents sample capacity, the number of devices, the number of years or the number of hundreds of years are selected according to the sample capacity of the supplier, z represents a z statistical constant corresponding to a confidence level of 1- α, and when the confidence level is 95%, the z statistical constant is 1.96.
And S106, correcting the credibility of each power grid supplier based on the initial quality scores of the power grid suppliers, and acquiring the actual ranking results of the power grid suppliers.
Specifically, according to the initial quality score of the power grid provider obtained in the implementation process of the embodiment of the present invention, the credibility of each provider is corrected, where the credibility is a value to be corrected, after the correction is completed, the providers can be sorted according to the corrected credibility, the credibility of each power grid provider is ranked from high to low by using the sorting module in the processor, and the ranking result is used as the final actual ranking result of multiple power grid providers and is output to the user side for use.
Optionally, the correcting the credibility of each power grid provider based on the initial quality scores of the plurality of power grid providers, and the obtaining the actual ranking results of the plurality of power grid providers includes: performing differential compression on the confidence interval radius of the power grid supplier by using a compression ratio model; and correcting the credibility of the power grid supplier based on the result of the differential compression.
Optionally, the compression ratio model is characterized by the following formula:
Figure BDA0002352062010000091
wherein p is the initial score of the supplier, Pdow is the lower limit of the confidence interval of p, namely the final score of the supplier, n represents the sample capacity, the number of devices, the number of years or the number of hundreds of years are selected according to the sample capacity of the supplier, z represents the z statistical constant corresponding to the confidence level of 1- α, and when the confidence level is 95%, the z statistical constant is 1.96.
Specifically, the basic index ranked by the supplier initial score is corrected by a confidence interval (i.e., confidence). The larger the sample capacity is, the narrower the confidence interval is, and the smaller the difference value between the lower limit value and the sample mean value is; the smaller the sample capacity is, the wider the confidence interval is, and the larger the difference value between the lower limit value and the sample mean value is, so that the accuracy problem of small sample capacity evaluation is well solved.
In order to solve the Martian effect existing in the traditional Wilson ranking algorithm, a compression ratio improved algorithm model is introduced, and a supplier is putThe radius of the information interval is compressed in a differentiation mode so as to promote the ranking of suppliers with small sample number and outstanding equipment quality. The compression ratio model comprehensively considers that the average deduction value lambda of each year in the evaluation fineness range is DΣ/T∑(DΣTo evaluate the total deduction, T, of all quality problems occurring within the range of finenessΣIn order to evaluate the total number of years of equipment of all suppliers in the fineness range), the average operating life x of the suppliers' equipment is T/N (T is the number of years of equipment of the suppliers, N is the number of equipment of the suppliers), the number of effective samples N of the suppliers and the deduction value is D, as follows:
Figure BDA0002352062010000092
the compression ratio model can differentially improve the ranking of high-quality suppliers, and in the same evaluation fineness, the lifting amplitude of the suppliers is in negative correlation with the deduction value of the suppliers and is in positive correlation with the sample number of the suppliers; the score increase of the supplier is positively correlated with the annual average deduction value of the evaluation fineness among different evaluation fineness.
The lower limit of the Wilson confidence interval after introducing the compression ratio is expressed as:
Figure BDA0002352062010000093
the method comprises the following steps of obtaining a confidence interval lower limit of Pdawn, wherein p is an initial score of a supplier, Pdawn is a confidence interval lower limit of p, namely a final score of the supplier, n represents sample capacity, the number of devices, the number of years or the number of hundreds of years are selected according to the sample capacity of the supplier, z represents a z statistical constant corresponding to a confidence level of 1- α, and when the confidence level is 95%, the z statistical constant is 1.96.
Optionally, after correcting the credibility of each power grid provider based on the initial quality scores of the plurality of power grid providers and obtaining the actual ranking results of the plurality of power grid providers, the method further includes: and taking the power grid supplier coverage power grid province number c as a sample number influence factor, and correcting the current reliability of the power grid supplier after repair by using the compression ratio model again.
Optionally, after obtaining the actual ranking results of the plurality of grid providers, the method further includes: and classifying the plurality of power grid suppliers according to the actual ranking result by using a ranking algorithm based on the confidence interval, and adjusting the number of classification categories in the classification result according to the equipment evaluation fineness and the number of suppliers.
Specifically, in order to make the actual ranking result more practical for the power grid supplier, the devices need to be classified into A, B, C, D, E five types from high to low according to a "wilson confidence interval" ranking algorithm, wherein the type a accounts for 10%; 20% of B type; class C accounts for 40%, class D accounts for 20%, and class E accounts for 10% (the number of classification classes can be adjusted according to the fineness of equipment evaluation and the number of suppliers). If the evaluation fineness of the supplier has familial defects judged by national network companies, the evaluation is reduced by level 1, and the validity period is 2 years.
The definition of A grade is 93-100 grades, B grade is 85-92 grades, C grade is 77-84 grades, D grade is 69-76 grades, and E grade is 60-68 grades. According to the grading situation, the equipment scores are subjected to piecewise linear function normalization, and the specific score value (percent system) of each supplier is finally obtained.
According to another aspect of the embodiments of the present invention, there is also provided a quality data processing apparatus, as shown in fig. 2, including: an obtaining module 20, configured to obtain initial ranking results of a plurality of power grid providers based on initial quality scores of the plurality of power grid providers, where the initial quality scores are determined based on at least one quality indicator of the power grid providers; the determining module 22 is used for determining the credibility of each power grid supplier based on the effective sample number of each power grid supplier; and the correcting module 24 is configured to correct the reliability of each power grid provider based on the initial quality scores of the multiple power grid providers, and obtain an actual ranking result of the multiple power grid providers.
An obtaining module 20 is configured to obtain an initial ranking result of the plurality of grid providers based on initial quality scores of the plurality of grid providers, wherein the initial quality scores are determined based on at least one quality indicator of the grid providers.
Specifically, initial quality scores of a plurality of power grid suppliers are obtained, the quality scores are determined by at least one quality index, all the quality scores are sorted by the processor, and ranking results are stored and output for subsequent processing and analysis processes.
Optionally, the quality index includes at least one of the following: the power grid supplier generates a defective deduction point, a non-stop deduction point, a fault deduction point and a reporting deduction point.
Comprehensively analyzing the factors such as the number of equipment, the equipment operation time, the quality defect, the fault, the non-stop and the quality event report, and the like, and formulating the following supplier scoring mechanism:
(1) counting of years
Let the initial and final years in the period in which the supplier evaluation was carried out be Yl and Yu, respectively. Then, in the supplier a with the number of shipped devices n, the number ti of device stations with a certain shipped year yi is:
Figure BDA0002352062010000111
total years TA for supplier a is:
Figure BDA0002352062010000112
(2) supplier initial score calculation
Starting from the equipment operation, the equipment operation score is increased in an arithmetic progression by an initial value s0 and a step length f, and the score sj in the j-th year is as follows:
sj＝s0+f×(j-1) (3)
if the device has defects in the jth' year, the year is divided into:
sj'＝sd(4)
wherein sd is the initial score of defect occurrence in the current year (different from the initial score of defect-free years), d is the correction deduction value (obtained according to the corresponding equipment evaluation guide), and k is the deduction coefficient (see formula 14 for details of values).
The next year of the defect of the equipment is regarded as the equipment is put into operation again, the score of the year is newly calculated from s0, and the operation score Si of the equipment after y years of the equipment operation is as follows:
Figure BDA0002352062010000113
in summary, the supplier device operation score S Σ is:
Figure BDA0002352062010000114
after the operation scores of the suppliers are obtained, considering the conditions of defects, faults, non-stop, quality event notification and the like of the suppliers, carrying out quantification processing on the operation scores:
the credit for the defect occurrence of the supplier is:
D＝∑d (7)
the calculated credit value for the non-stop in ns occurrences of the quotients is:
Ds＝ns×ds(8)
the calculated deduction value of the supplier for nf failures is:
Df＝nf×df(9)
the calculated deduction value of the supplier for nbp province company announcements is:
Dbp＝nbp×dbp(10)
the calculated credit value of the supplier for nbs times of notification by national network company is:
Dbs＝nbs×dbs(11)
the calculated deduction value of the supplier for the familial defect of the nd province company is as follows:
Dd＝nd×dd(12)
in the formulas (7) to (12), ds is a single deduction value of non-stop of equipment, df is a single deduction value of fault, dbp is a single deduction value notified by a company generating province, dbs is a single deduction value notified by a company generating country, and dd is a single deduction value of a family defect of the company generating province.
For a certain supplier with equipment years T, its initial score p is:
Figure BDA0002352062010000121
aiming at different deduction conditions of various equipment, a dynamic adjustment factor is introduced, so that the problem that the running condition or the defect fault condition of the equipment affects the evaluation result in a unilateral way is solved, and the quality condition of the equipment is comprehensively reflected by evaluation. Balancing the weight of the equipment operation score and the defect deduction value, and setting a deduction coefficient k:
Figure BDA0002352062010000122
where r is the ratio of the total score Σ (D + Df + Ds + Dbp + Dbs + Dd) for a certain type of evaluation fineness to the defect-free operation score Sp, and rmax and rmin are the maximum and minimum values, respectively, for all evaluation fineness.
2.2.3 calculating the number of valid samples
Let n be the number of samples of the ith supplier for a certain evaluation finenessiThe net province coverage is ciThen, the average net province sample number giIs ni/ciThe maximum average number of net provinces samples of the suppliers with the net province coverage reaching the threshold is gmaxAnd thus the number of valid samples neiCan be defined as:
Figure BDA0002352062010000123
from the above formula, when the net province coverage of a certain supplier does not reach the threshold of the fineness and the average net province sample number is larger than gmaxWhen the number of valid samples of the supplier is not equal to the number of original samples niAnd is equal to gmaxWith its net province coverage degree of ciTo reduce the number of samples the supplier participates in, thereby reducing its first wilson score.
A determining module 22, configured to determine a credibility of each grid provider based on the valid sample number of each grid provider.
Specifically, since the correction operation needs to be performed according to the credibility, in the embodiment of the present invention, in order to obtain the specific credibility of each grid provider, the number of valid samples of each grid provider needs to be utilized and analyzed.
Optionally, a confidence interval determined by the effective sample number of each grid provider is used as the confidence, wherein the larger the capacity of the effective sample number is, the narrower the corresponding confidence interval is, and the smaller the difference between the lower limit value and the average sample value is; the smaller the capacity of the effective sample number is, the wider the confidence interval is, and the larger the difference between the lower limit value and the sample mean value is.
Specifically, in order to obtain a confidence interval, namely a confidence interval, determined by the number of valid samples of each power grid provider, the embodiment of the invention adopts a wilson confidence interval ranking algorithm, and corrects the basic indexes of the provider initial score ranking through the confidence interval (namely, the confidence). The larger the sample capacity is, the narrower the confidence interval is, and the smaller the difference value between the lower limit value and the sample mean value is; the smaller the sample capacity is, the wider the confidence interval is, and the larger the difference value between the lower limit value and the sample mean value is, so that the accuracy problem of small sample capacity evaluation is well solved.
Optionally, before, the method further comprises: obtaining the sample number n of any power grid supplieriThe provincial coverage of the Hewei is ci(ii) a Based on the number of samples niThe provincial coverage of the Hewei is ciDetermining a valid number of samples n for the grid supplierei。
Specifically, in order to solve the Martian effect existing in the traditional Wilson ranking algorithm, a compression ratio improved algorithm model is introduced, and the confidence interval radius of a supplier is compressed in a differentiated mode, so that the ranking of the supplier with small sample number and outstanding equipment quality is improved. The compression ratio model comprehensively considers that the average deduction value lambda of each year in the evaluation fineness range is DΣ/T∑(DΣTo evaluate the total deduction, T, of all quality problems occurring within the range of finenessΣIn order to evaluate the total number of years of equipment of all suppliers in the fineness range), the average operating life x of the suppliers' equipment is T/N (T is the number of years of equipment of the suppliers, N is the number of equipment of the suppliers), the number of effective samples N of the suppliers and the deduction value D are as follows:
Figure BDA0002352062010000131
the compression ratio model can differentially improve the ranking of high-quality suppliers, and in the same evaluation fineness, the lifting amplitude of the suppliers is in negative correlation with the deduction value of the suppliers and is in positive correlation with the sample number of the suppliers; the score increase of the supplier is positively correlated with the annual average deduction value of the evaluation fineness among different evaluation fineness.
The lower limit of the Wilson confidence interval after introducing the compression ratio is expressed as:
Figure BDA0002352062010000141
the method comprises the following steps of obtaining a confidence interval lower limit of Pdawn, wherein p is an initial score of a supplier, Pdawn is a confidence interval lower limit of p, namely a final score of the supplier, n represents sample capacity, the number of devices, the number of years or the number of hundreds of years are selected according to the sample capacity of the supplier, z represents a z statistical constant corresponding to a confidence level of 1- α, and when the confidence level is 95%, the z statistical constant is 1.96.
And the correcting module 24 is configured to correct the reliability of each power grid provider based on the initial quality scores of the multiple power grid providers, and obtain actual ranking results of the multiple power grid providers.
Specifically, according to the initial quality score of the power grid provider obtained in the implementation process of the embodiment of the present invention, the credibility of each provider is corrected, where the credibility is a value to be corrected, after the correction is completed, the providers can be sorted according to the corrected credibility, the credibility of each power grid provider is ranked from high to low by using the sorting module in the processor, and the ranking result is used as the final actual ranking result of multiple power grid providers and is output to the user side for use.
Optionally, the correcting the credibility of each power grid provider based on the initial quality scores of the plurality of power grid providers, and the obtaining the actual ranking results of the plurality of power grid providers includes: performing differential compression on the confidence interval radius of the power grid supplier by using a compression ratio model; and correcting the credibility of the power grid supplier based on the result of the differential compression.
Optionally, the compression ratio model is characterized by the following formula:
Figure BDA0002352062010000142
wherein p is the initial score of the supplier, Pdow is the lower limit of the confidence interval of p, namely the final score of the supplier, n represents the sample capacity, the number of devices, the number of years or the number of hundreds of years are selected according to the sample capacity of the supplier, z represents the z statistical constant corresponding to the confidence level of 1- α, and when the confidence level is 95%, the z statistical constant is 1.96.
Specifically, the basic index ranked by the supplier initial score is corrected by a confidence interval (i.e., confidence). The larger the sample capacity is, the narrower the confidence interval is, and the smaller the difference value between the lower limit value and the sample mean value is; the smaller the sample capacity is, the wider the confidence interval is, and the larger the difference value between the lower limit value and the sample mean value is, so that the accuracy problem of small sample capacity evaluation is well solved.
In order to solve the Martian effect existing in the traditional Wilson ranking algorithm, a compression ratio improved algorithm model is introduced, and the confidence interval radius of a supplier is compressed in a differentiated mode, so that the ranking of the supplier with small sample number and outstanding equipment quality is improved. The compression ratio model comprehensively considers that the average deduction value lambda of each year in the evaluation fineness range is DΣ/T∑(DΣTo evaluate the total deduction, T, of all quality problems occurring within the range of finenessΣIn order to evaluate the total number of years of equipment of all suppliers in the fineness range), the average operating life x of the suppliers' equipment is T/N (T is the number of years of equipment of the suppliers, N is the number of equipment of the suppliers), the number of effective samples N of the suppliers and the deduction value is D, as follows:
Figure BDA0002352062010000151
the compression ratio model can differentially improve the ranking of high-quality suppliers, and in the same evaluation fineness, the lifting amplitude of the suppliers is in negative correlation with the deduction value of the suppliers and is in positive correlation with the sample number of the suppliers; the score increase of the supplier is positively correlated with the annual average deduction value of the evaluation fineness among different evaluation fineness.
The lower limit of the Wilson confidence interval after introducing the compression ratio is expressed as:
Figure BDA0002352062010000152
the method comprises the following steps of obtaining a confidence interval lower limit of Pdawn, wherein p is an initial score of a supplier, Pdawn is a confidence interval lower limit of p, namely a final score of the supplier, n represents sample capacity, the number of devices, the number of years or the number of hundreds of years are selected according to the sample capacity of the supplier, z represents a z statistical constant corresponding to a confidence level of 1- α, and when the confidence level is 95%, the z statistical constant is 1.96.
Optionally, after correcting the credibility of each power grid provider based on the initial quality scores of the plurality of power grid providers and obtaining the actual ranking results of the plurality of power grid providers, the method further includes: and taking the power grid supplier coverage power grid province number c as a sample number influence factor, and correcting the current reliability of the power grid supplier after repair by using the compression ratio model again.
Optionally, after obtaining the actual ranking results of the plurality of grid providers, the method further includes: and classifying the plurality of power grid suppliers according to the actual ranking result by using a ranking algorithm based on the confidence interval, and adjusting the number of classification categories in the classification result according to the equipment evaluation fineness and the number of suppliers.
Specifically, in order to make the actual ranking result more practical for the power grid supplier, the devices need to be classified into A, B, C, D, E five types from high to low according to a "wilson confidence interval" ranking algorithm, wherein the type a accounts for 10%; 20% of B type; class C accounts for 40%, class D accounts for 20%, and class E accounts for 10% (the number of classification classes can be adjusted according to the fineness of equipment evaluation and the number of suppliers). If the evaluation fineness of the supplier has familial defects judged by national network companies, the evaluation is reduced by level 1, and the validity period is 2 years.
The definition of A grade is 93-100 grades, B grade is 85-92 grades, C grade is 77-84 grades, D grade is 69-76 grades, and E grade is 60-68 grades. According to the grading situation, the equipment scores are subjected to piecewise linear function normalization, and the specific score value (percent system) of each supplier is finally obtained.
According to another aspect of the embodiments of the present invention, there is also provided a non-volatile storage medium, which is characterized in that the non-volatile storage medium includes a stored program, and the program controls a device in which the non-volatile storage medium is located to execute the processing method of the quality data when running.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including a processor and a memory; the memory is stored with computer readable instructions, and the processor is used for executing the computer readable instructions, wherein the computer readable instructions execute the processing method of the quality data.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.

Claims (11)

1. A method for processing quality data, comprising:
obtaining initial ranking results of a plurality of power grid providers based on initial quality scores of the plurality of power grid providers, wherein the initial quality scores are determined based on at least one quality index of the power grid providers;
determining the credibility of each power grid supplier based on the effective sample number of each power grid supplier;
and correcting the credibility of each power grid supplier based on the initial quality scores of the power grid suppliers, and acquiring the actual ranking results of the power grid suppliers.
2. The method of claim 1, wherein the quality indicator comprises at least one of: the power grid supplier generates a defective deduction point, a non-stop deduction point, a fault deduction point and a reporting deduction point.
3. The method according to claim 1, characterized in that a confidence interval determined by the effective sample number of each grid supplier is used as the confidence level, wherein the larger the capacity of the effective sample number is, the narrower the corresponding confidence interval is, and the smaller the difference between the lower limit value and the sample mean value is; the smaller the capacity of the effective sample number is, the wider the confidence interval is, and the larger the difference between the lower limit value and the sample mean value is.
4. The method of claim 2, wherein, prior to the determining, the method further comprises:
obtaining the sample number n of any power grid supplieriThe provincial coverage of the Hewei is ci；
Based on the number of samples niThe provincial coverage of the Hewei is ciDetermining a number n of valid samples of the grid supplierei。
5. The method of claim 2, wherein the step of modifying the credibility of each grid provider based on the initial quality scores of the plurality of grid providers comprises the steps of:
performing differential compression on the confidence interval radius of the power grid supplier by using a compression ratio model;
and correcting the credibility of the power grid supplier based on the result of the differential compression.
6. The method of claim 5, wherein the compression ratio model is characterized by the formula:
Figure FDA0002352061000000011
the method comprises the steps of obtaining a confidence interval lower limit of Pdawn, wherein p is an initial score of a supplier, Pdawn is a confidence interval lower limit of p, namely a final score of the supplier, n represents sample capacity, the number of devices, the number of years or the number of hundreds of years are selected according to the sample capacity of the supplier, z represents a z statistical constant corresponding to a confidence level of 1- α, and when the confidence level is 95%, the z statistical constant is 1.96.
7. The method according to any one of claims 5 to 6, wherein after the reliability of each grid provider is modified based on the initial quality scores of the grid providers, and the actual ranking results of the grid providers are obtained, the method further comprises:
and taking the power grid supplier coverage power grid province number c as a sample number influence factor, and correcting the current reliability of the power grid supplier after repair by using the compression ratio model again.
8. The method of claim 1, wherein after obtaining actual ranking results for a plurality of grid providers, the method further comprises:
and classifying the plurality of power grid suppliers according to the actual ranking result by using a ranking algorithm based on the confidence interval, and adjusting the number of classification categories in the classification result according to the equipment evaluation fineness and the number of suppliers.
9. An apparatus for processing quality data, comprising:
an obtaining module, configured to obtain initial ranking results of a plurality of power grid providers based on initial quality scores of the plurality of power grid providers, wherein the initial quality scores are determined based on at least one quality indicator of the power grid providers;
the determining module is used for determining the credibility of each power grid supplier based on the effective sample number of each power grid supplier;
and the correcting module is used for correcting the credibility of each power grid supplier based on the initial quality scores of the power grid suppliers and acquiring the actual ranking results of the power grid suppliers.
10. A non-volatile storage medium, comprising a stored program, wherein the program, when executed, controls a device in which the non-volatile storage medium is located to perform the method of processing quality data according to any one of claims 1 to 8.
11. An electronic device comprising a processor and a memory; the memory is stored with computer readable instructions, and the processor is used for executing the computer readable instructions, wherein the computer readable instructions execute the processing method of the quality data according to any one of claims 1 to 8.
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